Instructions to use hf-internal-testing/tiny-random-SiglipForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-SiglipForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SiglipForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-SiglipForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e0d20a71f1c6077fc86e69b44ee04cddd28e9c7bb6aef192a4b2fc0b9c65f39
- Size of remote file:
- 118 kB
- SHA256:
- b13ae6d6d1a2bb3f807e93e5ddcd0fb2dbbed241502453bbcd112a2fc1bfe051
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.